Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	144,312
2	Rhode Island	143,240
3	South Dakota	140,382
4	Utah	126,714
5	Tennessee	124,176
6	Arizona	121,099
7	Iowa	117,722
8	Wisconsin	115,867
9	Nebraska	115,470
10	South Carolina	115,225
11	Oklahoma	114,524
12	New Jersey	114,423
13	Arkansas	113,122
14	Delaware	111,799
15	Indiana	110,963
16	Alabama	110,826
17	Illinois	109,391
18	Kansas	108,419
19	Florida	108,056
20	New York	107,981
21	Idaho	107,485
22	Mississippi	106,753
23	Minnesota	106,651
24	Nevada	105,107
25	Montana	104,725
26	Wyoming	103,918
27	Georgia	103,525
28	Kentucky	102,975
29	Massachusetts	102,567
30	Texas	101,792
31	Louisiana	101,248
32	Missouri	100,271
33	Michigan	99,150
34	Connecticut	97,423
35	New Mexico	96,727
36	California	95,910
37	North Carolina	95,526
38	Alaska	95,178
39	Colorado	94,841
40	Ohio	94,270
41	Pennsylvania	94,263
42	West Virginia	89,996
43	Virginia	79,144
44	Maryland	76,104
45	New Hampshire	72,608
46	District of Columbia	69,285
47	Washington	57,453
48	Puerto Rico	53,951
49	Maine	50,423
50	Oregon	47,788
51	Vermont	38,814
52	Hawaii	24,744

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Colorado	83
2	Oregon	73
3	Arizona	70
4	Delaware	63
5	Maine	58
6	Utah	53
7	Illinois	49
8	Hawaii	44
9	Missouri	40
10	Pennsylvania	39
11	South Carolina	39
12	Indiana	38
13	New York	38
14	Minnesota	36
15	Mississippi	34
16	Puerto Rico	34
17	Georgia	33
18	Montana	33
19	Oklahoma	33
20	Washington	33
21	North Dakota	32
22	Maryland	30
23	Arkansas	27
24	Ohio	27
25	Virginia	27
26	New Jersey	26
27	Kentucky	24
28	Wisconsin	23
29	Massachusetts	18
30	Michigan	18
31	New Hampshire	18
32	Iowa	17
33	Vermont	16
34	California	14
35	Texas	12
36	Nebraska	11
37	South Dakota	10
38	North Carolina	2
39	Tennessee	1
40	Alabama	0
41	Alaska	0
42	Connecticut	0
43	District of Columbia	0
44	Florida	0
45	Idaho	0
46	Kansas	0
47	Louisiana	0
48	Nevada	0
49	New Mexico	0
50	Rhode Island	0
51	West Virginia	0
52	Wyoming	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,951
2	New York	2,711
3	Massachusetts	2,592
4	Rhode Island	2,556
5	Mississippi	2,458
6	Arizona	2,421
7	Connecticut	2,310
8	South Dakota	2,276
9	Louisiana	2,274
10	Alabama	2,273
11	Pennsylvania	2,129
12	Michigan	2,038
13	New Mexico	2,034
14	North Dakota	2,024
15	Indiana	2,023
16	Illinois	1,990
17	Arkansas	1,932
18	Iowa	1,919
19	Georgia	1,900
20	South Carolina	1,890
21	Oklahoma	1,842
22	Nevada	1,813
23	Tennessee	1,807
24	Texas	1,782
25	Kansas	1,755
26	Florida	1,712
27	Delaware	1,705
28	Ohio	1,699
29	District of Columbia	1,603
30	California	1,600
31	Maryland	1,590
32	Missouri	1,576
33	West Virginia	1,557
34	Kentucky	1,552
35	Montana	1,509
36	Wisconsin	1,349
37	Minnesota	1,333
38	Virginia	1,310
39	North Carolina	1,249
40	Wyoming	1,242
41	Nebraska	1,228
42	Idaho	1,170
43	Colorado	1,167
44	New Hampshire	995
45	Puerto Rico	783
46	Washington	762
47	Utah	718
48	Oregon	640
49	Maine	613
50	Alaska	481
51	Vermont	408
52	Hawaii	351

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Illinois	2
2	Colorado	1
3	Indiana	1
4	Kentucky	1
5	Maryland	1
6	Michigan	1
7	Minnesota	1
8	New Jersey	1
9	Oregon	1
10	South Dakota	1
11	Virginia	1
12	Wisconsin	1
13	Alabama	0
14	Alaska	0
15	Arizona	0
16	Arkansas	0
17	California	0
18	Connecticut	0
19	Delaware	0
20	District of Columbia	0
21	Florida	0
22	Georgia	0
23	Hawaii	0
24	Idaho	0
25	Iowa	0
26	Kansas	0
27	Louisiana	0
28	Maine	0
29	Massachusetts	0
30	Mississippi	0
31	Missouri	0
32	Montana	0
33	Nebraska	0
34	Nevada	0
35	New Hampshire	0
36	New Mexico	0
37	New York	0
38	North Carolina	0
39	North Dakota	0
40	Ohio	0
41	Oklahoma	0
42	Pennsylvania	0
43	Puerto Rico	0
44	Rhode Island	0
45	South Carolina	0
46	Tennessee	0
47	Texas	0
48	Utah	0
49	Vermont	0
50	Washington	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	392,133	1	99
Crowley	Colorado	363,966	2	99
Bent	Colorado	276,493	3	99
Dewey	South Dakota	250,339	4	99
Lincoln	Arkansas	246,238	5	99
Davidson	Tennessee	142,993	193	93
York	South Carolina	113,460	986	68
Richland	South Carolina	113,412	987	68
Orange	California	85,680	2261	28
Pierce	Washington	60,906	2833	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,350	2	99
Jerauld	South Dakota	7,948	3	99
Hancock	Georgia	7,922	4	99
Emporia city	Virginia	7,856	5	99
Orange	California	1,595	1848	41
Richland	South Carolina	1,373	2097	33
York	South Carolina	1,370	2101	33
Davidson	Tennessee	1,367	2106	32
Pierce	Washington	767	2734	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.